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Fan S, Zhang H, Meng Z, Li A, Luo Y, Liu Y. Comparing the diagnostic efficacy of optical coherence tomography and frozen section for margin assessment in breast-conserving surgery: a meta-analysis. J Clin Pathol 2024:jcp-2024-209597. [PMID: 38862215 DOI: 10.1136/jcp-2024-209597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 05/31/2024] [Indexed: 06/13/2024]
Abstract
AIMS This meta-analysis assessed the relative diagnostic accuracy of optical coherence tomography (OCT) versus frozen section (FS) in evaluating surgical margins during breast-conserving procedures. METHODS PubMed and Embase were searched for relevant studies published up to October 2023. The inclusion criteria encompassed studies evaluating the diagnostic accuracy of OCT or FS in patients undergoing breast-conserving surgery. Sensitivity and specificity were analysed using the DerSimonian and Laird method and subsequently transformed through the Freeman-Tukey double inverse sine method. RESULTS The meta-analysis encompassed 36 articles, comprising 16 studies on OCT and 20 on FS, involving 10 289 specimens from 8058 patients. The overall sensitivity of OCT was 0.93 (95% CI: 0.90 to 0.96), surpassing that of FS, which was 0.82 (95% CI: 0.71 to 0.92), indicating a significantly higher sensitivity for OCT (p=0.04). Conversely, the overall specificity of OCT was 0.89 (95% CI: 0.83 to 0.94), while FS exhibited a higher specificity at 0.97 (95% CI: 0.95 to 0.99), suggesting a superior specificity for FS (p<0.01). CONCLUSIONS Our meta-analysis reveals that OCT offers superior sensitivity but inferior specificity compared with FS in assessing surgical margins in breast-conserving surgery patients. Further larger well-designed prospective studies are needed, especially those employing a head-to-head comparison design. PROSPERO REGISTRATION NUMBER CRD42023483751.
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Affiliation(s)
- Shishun Fan
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Huirui Zhang
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhenyu Meng
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Ang Li
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yuqing Luo
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yueping Liu
- Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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2
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Jong LJS, Appelman JGC, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images. SENSORS (BASEL, SWITZERLAND) 2024; 24:1567. [PMID: 38475103 DOI: 10.3390/s24051567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
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Affiliation(s)
- Lynn-Jade S Jong
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Jelmer G C Appelman
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands
| | - Henricus J C M Sterenborg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo J M Ruers
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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3
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Fan Y, Liu S, Gao E, Guo R, Dong G, Li Y, Gao T, Tang X, Liao H. The LMIT: Light-mediated minimally-invasive theranostics in oncology. Theranostics 2024; 14:341-362. [PMID: 38164160 PMCID: PMC10750201 DOI: 10.7150/thno.87783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/18/2023] [Indexed: 01/03/2024] Open
Abstract
Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.
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Affiliation(s)
- Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Shuai Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Enze Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Guozhao Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Yangxi Li
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
| | - Tianxin Gao
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China, 100081
| | - Hongen Liao
- Dept. of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 100084
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4
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Jagadeesan K, Palanisamy G. Atherosclerosis plaque tissue classification using self-attention-based conditional variational auto-encoder generative adversarial network using OCT plaque image. BIOMED ENG-BIOMED TE 2023; 68:633-649. [PMID: 37401612 DOI: 10.1515/bmt-2022-0286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 05/08/2023] [Indexed: 07/05/2023]
Abstract
Adults with coronary artery disease often have atherosclerosis, this is defined as the accumulation of plaque in the tissues of the arterial wall. Cardiologists utilize optical coherence tomography (OCT), a light-based imaging method, to examine the layers of intracoronary tissue along pathological formations, such as plaque accumulation. Intracoronary cross-sectional images produced by state-of-the-art catheter-based imaging scheme have 10-15 µm high resolution. Nevertheless, interpretation of the obtained images depends on the operator, which takes a lot of time and is exceedingly error-prone from one observer to another. OCT image post-processing that automatically and accurately tags coronary plaques can help the technique become more widely used and lower the diagnostic error rate. To overcome these problems, Atherosclerosis plaque tissue classification using Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Network (APC-OCTPI-SACVAGAN) is proposed which classifies the Atherosclerosis plaque images as Fibro calcific plaque, Fibro atheroma, Thrombus, Fibrous plaque and Micro-vessel. The proposed APC-OCTPI-SACVAGAN technique is executed in MATLAB. The efficiency of proposed APC-OCTPI-SACVAGAN method attains 16.19 %, 17.93 %, 19.81 % and 1.57 % higher accuracy; 16.92 %, 11.54 %, 5.29 % and 1.946 % higher Area under curve; and 28.06 %, 25.32 %, 32.19 % and 39.185 % lower computational time comparing to the existing methods respectively.
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Affiliation(s)
- Kowsalyadevi Jagadeesan
- Research Scholar, Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
| | - Geetha Palanisamy
- Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
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5
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Liu HC, Lin MH, Chang WC, Zeng RC, Wang YM, Sun CW. Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography. Cancers (Basel) 2023; 15:5388. [PMID: 38001648 PMCID: PMC10670228 DOI: 10.3390/cancers15225388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/02/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023] Open
Abstract
The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.
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Affiliation(s)
- Hung-Chang Liu
- Section of Thoracic Surgery, Mackay Memorial Hospital, Taipei City 10449, Taiwan;
- Intensive Care Unit, Mackay Memorial Hospital, Taipei City 10449, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City 25245, Taiwan
- Department of Optometry, Mackay Junior College of Medicine, Nursing, and Management, Taipei City 11260, Taiwan
| | - Miao-Hui Lin
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan; (M.-H.L.); (R.-C.Z.); (Y.-M.W.)
| | - Wei-Chin Chang
- Department of Pathology, Mackay Memorial Hospital, New Taipei City 25160, Taiwan;
- Department of Pathology, Taipei Medical University Hospital, Taipei City 11030, Taiwan
- Department of Pathology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City 11030, Taiwan
| | - Rui-Cheng Zeng
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan; (M.-H.L.); (R.-C.Z.); (Y.-M.W.)
| | - Yi-Min Wang
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan; (M.-H.L.); (R.-C.Z.); (Y.-M.W.)
| | - Chia-Wei Sun
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan; (M.-H.L.); (R.-C.Z.); (Y.-M.W.)
- Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan
- Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei City 11259, Taiwan
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6
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Duan Y, Guo D, Zhang X, Lan L, Meng H, Wang Y, Sui C, Qu Z, He G, Wang C, Liu X. Diagnostic accuracy of optical coherence tomography for margin assessment in breast-conserving surgery: A systematic review and meta-analysis. Photodiagnosis Photodyn Ther 2023; 43:103718. [PMID: 37482370 DOI: 10.1016/j.pdpdt.2023.103718] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/06/2023] [Accepted: 07/21/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Breast cancer is the most common malignant tumor among women, and its incidence is increasing annually. At present, the results of the study on whether optical coherence tomography (OCT) can be used as an intraoperative margin assessment method for breast-conserving surgery (BCS) are inconsistent. We herein conducted this systematic review and meta-analysis to assess the diagnostic value of OCT in BCS. METHODS PubMed, Web of Science, Cochrane Library, and Embase were used to search relevant studies published up to September 15, 2022. We used Review Manager 5.4, Meta-Disc 1.4, and STATA 16.0 for statistical analysis. RESULTS The results displayed 18 studies with 782 patients included according to the inclusion and exclusion criteria. Meta-analysis showed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and the area under the curve (AUC) of OCT in the margin assessment of BCS were 0.91 (95% CI 0.88-0.93), 0.88 (95% CI 0.83-0.92), 7.53 (95% CI 5.19-10.93), 0.11(95% CI 0.08-0.14), 70.37 (95% CI 39.78-124.47), and 0.94 (95% CI 0.92-0.96), respectively. CONCLUSIONS OCT is a promising technique in intraoperative margin assessment of breast cancer patients.
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Affiliation(s)
- Yuqing Duan
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Dingjie Guo
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Xin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Linwei Lan
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Hengyu Meng
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Yashan Wang
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Chuanying Sui
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Zihan Qu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Guangliang He
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China
| | - Chunpeng Wang
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China.
| | - Xin Liu
- Department of Epidemiology and Statistics, School of Public Health, Jilin University, Changchun, Jilin, China.
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7
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Wolff LI, Hachgenei E, Goßmann P, Druzenko M, Frye M, König N, Schmitt RH, Chrysos A, Jöchle K, Truhn D, Kather JN, Lambertz A, Gaisa NT, Jonigk D, Ulmer TF, Neumann UP, Lang SA, Amygdalos I. Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo. J Cancer Res Clin Oncol 2023; 149:7877-7885. [PMID: 37046121 PMCID: PMC10374764 DOI: 10.1007/s00432-023-04742-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/02/2023] [Indexed: 04/14/2023]
Abstract
PURPOSE Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. METHODS Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. RESULTS Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. CONCLUSION Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.
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Affiliation(s)
- Laura I Wolff
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Enno Hachgenei
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Paul Goßmann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Mariia Druzenko
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Maik Frye
- Department of Production Quality, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Niels König
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H Schmitt
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
- Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
| | - Alexandros Chrysos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Katharina Jöchle
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Internal Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav, Carus Technical University Dresden, Dresden, Germany
| | - Andreas Lambertz
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Nadine T Gaisa
- Institute for Pathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Danny Jonigk
- Institute for Pathology, University Hospital RWTH Aachen, Aachen, Germany
- German Center of Lungs Research (DZL, BREATH), Gießen, Germany
| | - Tom F Ulmer
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Ulf P Neumann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven A Lang
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Iakovos Amygdalos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany.
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8
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Scholler J, Mandache D, Mathieu MC, Lakhdar AB, Darche M, Monfort T, Boccara C, Olivo-Marin JC, Grieve K, Meas-Yedid V, la Guillaume EBA, Thouvenin O. Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning. J Med Imaging (Bellingham) 2023; 10:034504. [PMID: 37274760 PMCID: PMC10234284 DOI: 10.1117/1.jmi.10.3.034504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration. Approach We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual 1.3 × 1.3 mm 2 images and compared with standard H&E histology diagnosis. Results Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3 × 1.3 mm 2 ) and above 96% at the specimen level (above cm 2 ). Conclusions Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.
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Affiliation(s)
- Jules Scholler
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Diana Mandache
- AQUYRE Bioscences-LLTech SAS, Paris, France
- Institut Pasteur, Bioimage Analysis Unit, Paris, France
| | - Marie Christine Mathieu
- Gustave Roussy Cancer Campus, Department of Medical Biology and Pathology, Villejuif, France
| | | | - Marie Darche
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
| | - Tual Monfort
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Claude Boccara
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | | | - Kate Grieve
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
- Quinze-Vingts National Eye Hospital, Paris, France
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9
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Mojahed D, Applegate MB, Guo H, Taback B, Ha R, Hibshoosh H, Hendon CP. Optical coherence tomography holds promise to transform the diagnostic anatomic pathology gross evaluation process. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220102GR. [PMID: 36050827 PMCID: PMC9434002 DOI: 10.1117/1.jbo.27.9.096003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
SIGNIFICANCE Real-time histology can close a variety of gaps in tissue diagnostics. Currently, gross pathology analysis of excised tissue is dependent upon visual inspection and palpation to identify regions of interest for histopathological processing. Such analysis is limited by the variable correlation between macroscopic and microscopic findings. The current standard of care is costly, burdensome, and inefficient. AIM We are the first to address this gap by introducing optical coherence tomography (OCT) to be integrated in real-time during the pathology grossing process. APPROACH This is achieved by our high-resolution, ultrahigh-speed, large field-of-view OCT device designed for this clinical application. RESULTS We demonstrate the feasibility of imaging tissue sections from multiple human organs (breast, prostate, lung, and pancreas) in a clinical gross pathology setting without interrupting standard workflows. CONCLUSIONS OCT-based real-time histology evaluation holds promise for addressing a gap that has been present for >100 years.
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Affiliation(s)
- Diana Mojahed
- Columbia University, Department of Biomedical Engineering, New York, United States
- Columbia University, Department of Electrical Engineering, New York, United States
| | - Matthew B. Applegate
- Columbia University, Department of Electrical Engineering, New York, United States
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Hua Guo
- Columbia University Irving Medical Center, Department of Pathology, New York, United States
| | - Bret Taback
- Columbia University Irving Medical Center, Department of Surgery, New York, United States
| | - Richard Ha
- Columbia University Irving Medical Center, Department of Radiology, New York, United States
| | - Hanina Hibshoosh
- Columbia University Irving Medical Center, Department of Pathology, New York, United States
| | - Christine P. Hendon
- Columbia University, Department of Electrical Engineering, New York, United States
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10
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Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images. Sci Rep 2022; 12:13995. [PMID: 35978040 PMCID: PMC9385745 DOI: 10.1038/s41598-022-18393-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/10/2022] [Indexed: 12/26/2022] Open
Abstract
The dominant consequence of irradiating biological systems is cellular damage, yet microvascular damage begins to assume an increasingly important role as the radiation dose levels increase. This is currently becoming more relevant in radiation medicine with its pivot towards higher-dose-per-fraction/fewer fractions treatment paradigm (e.g., stereotactic body radiotherapy (SBRT)). We have thus developed a 3D preclinical imaging platform based on speckle-variance optical coherence tomography (svOCT) for longitudinal monitoring of tumour microvascular radiation responses in vivo. Here we present an artificial intelligence (AI) approach to analyze the resultant microvascular data. In this initial study, we show that AI can successfully classify SBRT-relevant clinical radiation dose levels at multiple timepoints (t = 2–4 weeks) following irradiation (10 Gy and 30 Gy cohorts) based on induced changes in the detected microvascular networks. Practicality of the obtained results, challenges associated with modest number of animals, their successful mitigation via augmented data approaches, and advantages of using 3D deep learning methodologies, are discussed. Extension of this encouraging initial study to longitudinal AI-based time-series analysis for treatment outcome predictions at finer dose level gradations is envisioned.
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11
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Yang L, Chen Y, Ling S, Wang J, Wang G, Zhang B, Zhao H, Zhao Q, Mao J. Research progress on the application of optical coherence tomography in the field of oncology. Front Oncol 2022; 12:953934. [PMID: 35957903 PMCID: PMC9358962 DOI: 10.3389/fonc.2022.953934] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 06/29/2022] [Indexed: 11/25/2022] Open
Abstract
Optical coherence tomography (OCT) is a non-invasive imaging technique which has become the “gold standard” for diagnosis in the field of ophthalmology. However, in contrast to the eye, nontransparent tissues exhibit a high degree of optical scattering and absorption, resulting in a limited OCT imaging depth. And the progress made in the past decade in OCT technology have made it possible to image nontransparent tissues with high spatial resolution at large (up to 2mm) imaging depth. On the one hand, OCT can be used in a rapid, noninvasive way to detect diseased tissues, organs, blood vessels or glands. On the other hand, it can also identify the optical characteristics of suspicious parts in the early stage of the disease, which is of great significance for the early diagnosis of tumor diseases. Furthermore, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This review summarizes the recent advances in the OCT area, which application in oncological diagnosis and treatment in different types: (1) superficial tumors:OCT could detect microscopic information on the skin’s surface at high resolution and has been demonstrated to help diagnose common skin cancers; (2) gastrointestinal tumors: OCT can be integrated into small probes and catheters to image the structure of the stomach wall, enabling the diagnosis and differentiation of gastrointestinal tumors and inflammation; (3) deep tumors: with the rapid development of OCT imaging technology, it has shown great potential in the diagnosis of deep tumors such in brain tumors, breast cancer, bladder cancer, and lung cancer.
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Affiliation(s)
- Linhai Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
| | - Yulun Chen
- School of Medicine, Xiamen University, Xiamen, China
| | - Shuting Ling
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
| | - Jing Wang
- Department of Imaging, School of Medicine, Xiamen Cardiovascular Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Guangxing Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
| | - Bei Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
| | - Hengyu Zhao
- Department of Imaging, School of Medicine, Xiamen Cardiovascular Hospital of Xiamen University, Xiamen University, Xiamen, China
- *Correspondence: Hengyu Zhao, ; Qingliang Zhao, ; Jingsong Mao,
| | - Qingliang Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
- *Correspondence: Hengyu Zhao, ; Qingliang Zhao, ; Jingsong Mao,
| | - Jingsong Mao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, Department of Laboratory Medicine, School of Public Health, Shenzhen Research Institute of Xiamen University, Xiamen University, Xiamen, China
- Department of Radiology, Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, Xiamen, China
- *Correspondence: Hengyu Zhao, ; Qingliang Zhao, ; Jingsong Mao,
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12
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Foo KY, Newman K, Fang Q, Gong P, Ismail HM, Lakhiani DD, Zilkens R, Dessauvagie BF, Latham B, Saunders CM, Chin L, Kennedy BF. Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning. BIOMEDICAL OPTICS EXPRESS 2022; 13:3380-3400. [PMID: 35781967 PMCID: PMC9208580 DOI: 10.1364/boe.455110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 05/27/2023]
Abstract
We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.
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Affiliation(s)
- Ken Y. Foo
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Kyle Newman
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Qi Fang
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Peijun Gong
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Hina M. Ismail
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Devina D. Lakhiani
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Renate Zilkens
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Division of Surgery, Medical School, The University of Western Australia, Perth, WA 6009, Australia
| | - Benjamin F. Dessauvagie
- Division of Pathology and Laboratory Medicine, Medical School, The University of Western Australia, Perth, WA 6009, Australia
- PathWest, Fiona Stanley Hospital, Murdoch, WA 6150, Australia
| | - Bruce Latham
- PathWest, Fiona Stanley Hospital, Murdoch, WA 6150, Australia
- School of Medicine, The University of Notre Dame, Fremantle, WA 6160, Australia
| | - Christobel M. Saunders
- Division of Surgery, Medical School, The University of Western Australia, Perth, WA 6009, Australia
- Breast Centre, Fiona Stanley Hospital, Murdoch, WA 6150, Australia
- Breast Clinic, Royal Perth Hospital, Perth, WA 6000, Australia
- Department of Surgery, Melbourne Medical School, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Lixin Chin
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
| | - Brendan F. Kennedy
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
- Department of Electrical, Electronic & Computer Engineering, School of Engineering, The University of Western Australia, Perth, WA 6009, Australia
- Australian Research Council Centre for Personalised Therapeutics Technologies, Perth, WA 6000, Australia
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13
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Bareja R, Mojahed D, Hibshoosh H, Hendon C. Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks. APPLIED OPTICS 2022; 61:4458-4462. [PMID: 36256284 DOI: 10.1364/ao.455626] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/29/2022] [Indexed: 06/16/2023]
Abstract
Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolution OCT system with 2.72 µm axial resolution and 5.52 µm lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration of OCT into clinical practice.
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Yaari Z, Horoszko CP, Antman-Passig M, Kim M, Nguyen FT, Heller DA. Emerging technologies in cancer detection. Cancer Biomark 2022. [DOI: 10.1016/b978-0-12-824302-2.00011-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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Li W, Li X. Development of intraoperative assessment of margins in breast conserving surgery: a narrative review. Gland Surg 2022; 11:258-269. [PMID: 35242687 PMCID: PMC8825505 DOI: 10.21037/gs-21-652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/17/2021] [Indexed: 07/28/2023]
Abstract
OBJECTIVE We intend to provide an informative and up-to-date summary on the topic of intraoperative assessment of margins in breast conserving surgery (BCS). Conventional methods as well as cutting-edge technologies are analyzed for their advantages and limitations in the hope that clinicians can turn to this for reference. This review can also offer guidance for technicians in the future design of intraoperative margin assessment tools. BACKGROUND Achieving negative margins during BCS is one of the vital factors for preventing local recurrence. Conducting intraoperative margin assessment can ensure negative margins to a large extent and possibly relieve patients of the anguish of re-interventions. In recent years, innovative methods for margin assessment during BCS are advancing rapidly. And there is a lack of summary regarding the development of intraoperative margin assessment in BCS. METHODS A PubMed search with keywords "intraoperative margin assessment" and "breast conserving surgery" was conducted. Relevant publications were screened manually for its title, abstract and even full text to determine its true relevance. Publications on neo-adjuvant therapy and intraoperative radiotherapy were excluded. References from the searched articles and other supplementary articles were also looked into. CONCLUSIONS Conventional methods for margin assessment yields stable outcome but its use is limited because of the demand on pathology staff and the trade-off between time and precision. Conventional imaging techniques pass the workload to radiologists at the cost of a significantly low duration of time. Involving artificial intelligence for image-based assessment is a further improvement. However, conventional imaging is inherently flawed in that occult lesions can't show on the image and the showing ones are ambiguous and open to interpretation. Unconventional techniques which base their judgment on cellular composition are more reassuring. Nonetheless, unconventional techniques should be subjected to clinical trials before putting into practice. And studies regarding comparison between conventional methods and unconventional methods are also needed to evaluate their relative efficacy.
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Affiliation(s)
- Wanheng Li
- First Clinical Medical School, Southern Medical University, Guangzhou, China
| | - Xiru Li
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
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16
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Mewada H, Al-Asad JF, Patel A, Chaudhari J, Mahant K, Vala A. Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification. Open Biomed Eng J 2021. [DOI: 10.2174/1874120702115010132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Background:
The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features.
Methods:
The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images.
Result:
The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score.
Conclusion:
LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy.
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17
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Ji X, Mojahed D, Okawachi Y, Gaeta AL, Hendon CP, Lipson M. Millimeter-scale chip-based supercontinuum generation for optical coherence tomography. SCIENCE ADVANCES 2021; 7:eabg8869. [PMID: 34533990 PMCID: PMC8448444 DOI: 10.1126/sciadv.abg8869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Supercontinuum sources for optical coherence tomography (OCT) have raised great interest as they provide broad bandwidth to enable high resolution and high power to improve imaging sensitivity. Commercial fiber-based supercontinuum systems require high pump powers to generate broad bandwidth and customized optical filters to shape/attenuate the spectra. They also have limited sensitivity and depth performance. We introduce a supercontinuum platform based on a 1-mm2 Si3N4 photonic chip for OCT. We directly pump and efficiently generate supercontinuum near 1300 nm without any postfiltering. With a 25-pJ pump pulse, we generate a broadband spectrum with a flat 3-dB bandwidth of 105 nm. Integrating the chip into a spectral domain OCT system, we achieve 105-dB sensitivity and 1.81-mm 6-dB sensitivity roll-off with 300-μW optical power on sample. We image breast tissue to demonstrate strong imaging performance. Our chip will pave the way toward portable OCT and incorporating integrated photonics into optical imaging technologies.
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Affiliation(s)
- Xingchen Ji
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | - Diana Mojahed
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Yoshitomo Okawachi
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, USA
| | - Alexander L. Gaeta
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, USA
| | - Christine P. Hendon
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
- Corresponding author. (M.L.); (C.P.H.)
| | - Michal Lipson
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
- Corresponding author. (M.L.); (C.P.H.)
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18
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Hsiao T, Ho Y, Chen M, Lee S, Sun C. Disease activation maps for subgingival dental calculus identification based on intelligent dental optical coherence tomography. TRANSLATIONAL BIOPHOTONICS 2021. [DOI: 10.1002/tbio.202100001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Affiliation(s)
- Tien‐Yu Hsiao
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan, ROC
| | - Yi‐Ching Ho
- School of Dentistry National Yang Ming Chiao Tung University Taipei Taiwan, ROC
- Department of Stomatology Taipei Veterans General Hospital Taipei Taiwan, ROC
| | - Mei‐Ru Chen
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan, ROC
| | - Shyh‐Yuan Lee
- School of Dentistry National Yang Ming Chiao Tung University Taipei Taiwan, ROC
- Department of Stomatology Taipei Veterans General Hospital Taipei Taiwan, ROC
- Department of Dentistry Yangming Branch of Taipei City Hospital Taipei Taiwan, ROC
| | - Chia‐Wei Sun
- Biomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan, ROC
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McLean JP, Hendon CP. 3-D compressed sensing optical coherence tomography using predictive coding. BIOMEDICAL OPTICS EXPRESS 2021; 12:2531-2549. [PMID: 33996246 PMCID: PMC8086477 DOI: 10.1364/boe.421848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/24/2021] [Accepted: 03/25/2021] [Indexed: 05/05/2023]
Abstract
We present a compressed sensing (CS) algorithm and sampling strategy for reconstructing 3-D Optical Coherence Tomography (OCT) image volumes from as little as 10% of the original data. Reconstruction using the proposed method, Denoising Predictive Coding (DN-PC), is demonstrated for five clinically relevant tissue types including human heart, retina, uterus, breast, and bovine ligament. DN-PC reconstructs the difference between adjacent b-scans in a volume and iteratively applies Gaussian filtering to improve image sparsity. An a-line sampling strategy was developed that can be easily implemented in existing Spectral-Domain OCT systems and reduce scan time by up to 90%.
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Foo KY, Kennedy KM, Zilkens R, Allen WM, Fang Q, Sanderson RW, Anstie J, Dessauvagie BF, Latham B, Saunders CM, Chin L, Kennedy BF. Optical palpation for tumor margin assessment in breast-conserving surgery. BIOMEDICAL OPTICS EXPRESS 2021; 12:1666-1682. [PMID: 33796380 PMCID: PMC7984801 DOI: 10.1364/boe.415888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
Intraoperative margin assessment is needed to reduce the re-excision rate of breast-conserving surgery. One possibility is optical palpation, a tactile imaging technique that maps stress (force applied across the tissue surface) as an indicator of tissue stiffness. Images (optical palpograms) are generated by compressing a transparent silicone layer on the tissue and measuring the layer deformation using optical coherence tomography (OCT). This paper reports, for the first time, the diagnostic accuracy of optical palpation in identifying tumor within 1 mm of the excised specimen boundary using an automated classifier. Optical palpograms from 154 regions of interest (ROIs) from 71 excised tumor specimens were obtained. An automated classifier was constructed to predict the ROI margin status by first choosing a circle diameter, then searching for a location within the ROI where the circle was ≥ 75% filled with high stress (indicating a positive margin). A range of circle diameters and stress thresholds, as well as the impact of filtering out non-dense tissue regions, were tested. Sensitivity and specificity were calculated by comparing the automated classifier results with the true margin status, determined from co-registered histology. 83.3% sensitivity and 86.2% specificity were achieved, compared to 69.0% sensitivity and 79.0% specificity obtained with OCT alone on the same dataset using human readers. Representative optical palpograms show that positive margins containing a range of cancer types tend to exhibit higher stress compared to negative margins. These results demonstrate the potential of optical palpation for margin assessment.
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Affiliation(s)
- Ken Y. Foo
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - Kelsey M. Kennedy
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
| | - Renate Zilkens
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- School of Medicine, The University of Western Australia, Perth, Australia
| | - Wes M. Allen
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - Qi Fang
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - Rowan W. Sanderson
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - James Anstie
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - Benjamin F. Dessauvagie
- School of Medicine, The University of Western Australia, Perth, Australia
- PathWest, Fiona Stanley Hospital, Murdoch, Australia
| | - Bruce Latham
- PathWest, Fiona Stanley Hospital, Murdoch, Australia
- School of Medicine, University of Notre Dame, Fremantle, Australia
| | - Christobel M. Saunders
- School of Medicine, The University of Western Australia, Perth, Australia
- Breast Centre, Fiona Stanley Hospital, Murdoch, Australia
- Breast Clinic, Royal Perth Hospital, Perth, Australia
| | - Lixin Chin
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
| | - Brendan F. Kennedy
- BRITElab, Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Australia
- The University of Western Australia, Perth, Australia
- Department of Electrical, Electronic and Computer Engineering, School of Engineering, The University of Western Australia, Perth, Australia
- Australian Research Council Centre for Personalised Therapeutics Technologies, Perth, Australia
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Ellebrecht DB, Latus S, Schlaefer A, Keck T, Gessert N. Towards an Optical Biopsy during Visceral Surgical Interventions. Visc Med 2020; 36:70-79. [PMID: 32355663 DOI: 10.1159/000505938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 01/13/2020] [Indexed: 12/24/2022] Open
Abstract
Background Cancer will replace cardiovascular diseases as the most frequent cause of death. Therefore, the goals of cancer treatment are prevention strategies and early detection by cancer screening and ideal stage therapy. From an oncological point of view, complete tumor resection is a significant prognostic factor. Optical coherence tomography (OCT) and confocal laser microscopy (CLM) are two techniques that have the potential to complement intraoperative frozen section analysis as in vivo and real-time optical biopsies. Summary In this review we present both procedures and review the progress of evaluation for intraoperative application in visceral surgery. For visceral surgery, there are promising studies evaluating OCT and CLM; however, application during routine visceral surgical interventions is still lacking. Key Message OCT and CLM are not competing but complementary approaches of tissue analysis to intraoperative frozen section analysis. Although intraoperative application of OCT and CLM is at an early stage, they are two promising techniques of intraoperative in vivo and real-time tissue examination. Additionally, deep learning strategies provide a significant supplement for automated tissue detection.
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Affiliation(s)
- David Benjamin Ellebrecht
- LungenClinic Grosshansdorf, Department of Thoracic Surgery, Grosshansdorf, Germany.,University Medical Center Schleswig-Holstein, Campus Lübeck, Department of Surgery, Lübeck, Germany
| | - Sarah Latus
- Hamburg University of Technology, Institute of Medical Technology, Hamburg, Germany
| | - Alexander Schlaefer
- Hamburg University of Technology, Institute of Medical Technology, Hamburg, Germany
| | - Tobias Keck
- University Medical Center Schleswig-Holstein, Campus Lübeck, Department of Surgery, Lübeck, Germany
| | - Nils Gessert
- Hamburg University of Technology, Institute of Medical Technology, Hamburg, Germany
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